SIMULATION OF GRAVITATIONAL SOLIDS FLOW PROCESS AND ITS PARAMETERS ESTIMATION BY THE USE OF ELECTRICAL CAPACITANCE TOMOGRAPHY AND ARTIFICIAL NEURAL NETWORKS

Abstract

The paper presents a new approach to monitoring changes of characteristic parameters of gravitational solids flow. Electrical Capacitance Tomography (ECT) is applied for non-invasive process monitoring. Artificial Neural Networks (ANN) are used to estimate important flow parameters knowing the measured capacitances. The proposed approach solves the ECT inverse problem in a direct manner and provides a rapid parameterization of the funnel flow. The simulation of the silo discharging process is performed relying on real flow behaviour obtained from the authors’ previous work. The simulated data are used to new approach testing and verification. The obtained results proved that proposed ANN-based method will allow for on-line gravitational solids flow monitoring.

Keywords

• Electrical Capacitance Tomography
• process simulation
• Artificial Neural Networks
• funnel flow parameters estimation